Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

被引:111
作者
Xue, Dezhen [1 ,2 ]
Balachandran, Prasanna V. [1 ]
Yuan, Ruihao [2 ]
Hu, Tao [3 ]
Qian, Xiaoning [3 ]
Dougherty, Edward R. [3 ]
Lookman, Turab [1 ]
机构
[1] Los Alamos Natl Lab, Div Theoret, Los Alamos, NM 87545 USA
[2] Xi An Jiao Tong Univ, State Key Lab Mech Behav Mat, Xian 710049, Peoples R China
[3] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA
基金
美国国家科学基金会;
关键词
piezoelectric materials; materials informatics; Bayesian learning; morphotropic phase boundary; Pb-free materials; SPONTANEOUS POLARIZATION; TEMPERATURE; DESIGN; CLASSIFICATION; GENE;
D O I
10.1073/pnas.1607412113
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
An outstanding challenge in the nascent field of materials informatics is to incorporate materials knowledge in a robust Bayesian approach to guide the discovery of new materials. Utilizing inputs from known phase diagrams, features or material descriptors that are known to affect the ferroelectric response, and Landau-Devonshire theory, we demonstrate our approach for BaTiO3-based piezoelectrics with the desired target of a vertical morphotropic phase boundary. We predict, synthesize, and characterize a solid solution, (Ba0.5Ca0.5)TiO3-Ba(Ti0.7Zr0.3)O-3, with piezoelectric properties that show better temperature reliability than other BaTiO3-based piezoelectrics in our initial training data.
引用
收藏
页码:13301 / 13306
页数:6
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